code: def forward(self, x, x_mask):
      x_res = x
      for i in range(self.num_layers):
          x = self.conv_layers[i](x * x_mask)
          x = self.norm_layers[i](x * x_mask)
          x = F.dropout(
              F.relu(x), self.dropout_p, training=self.training
          )
      x = x_res + self.proj(x)
      return x * x_mask
  
file_path: TTS/TTS/tts/layers/glow_tts/glow.py
imports: import torch
  from torch import nn
  from torch.nn import functional
  from TTS.tts.layers.generic.wavenet import WN
  from ..generic.normalization import LayerNorm
label_desc: Use image node_id 2 for calling the ResidualConv1dLayerNormBlock obj's underlying member method code with example usage: obj.forward(x, x_mask) and returns: unknown